Meta-Learning and Few-Shot Adaptation in Foundation Models
Chapter 1: Foundations of Meta-Learning Revisited
The Meta-Learning Problem Formulation
Taxonomy of Meta-Learning Approaches
Challenges in Applying Meta-Learning to Foundation Models
Evaluation Protocols for Few-Shot Learning
Chapter 2: Advanced Gradient-Based Meta-Learning
Model-Agnostic Meta-Learning (MAML) Deep Dive
First-Order MAML (FOMAML) and Reptile
Implicit MAML (iMAML)
Addressing Stability and Gradient Variance
Scalability Considerations for Foundation Models
Hands-on Practical: Implementing FOMAML for Model Adaptation
Chapter 3: Advanced Metric-Based Meta-Learning
Prototypical Networks Revisited
Relation Networks for Few-Shot Learning
Matching Networks with Attention
Deep Metric Learning Techniques
Adapting Metric Learning for High-Dimensional Embeddings
Practice: Implementing Prototypical Networks with Foundation Model Embeddings
Chapter 4: Optimization Perspectives on Meta-Learning
Meta-Learning as Bilevel Optimization
Algorithms for Solving Bilevel Problems
Connections to Hyperparameter Optimization
Meta-Learning Initialization Strategies
Theoretical Convergence Analysis
Chapter 5: Few-Shot Adaptation Strategies for Foundation Models
Parameter-Efficient Fine-Tuning (PEFT) Overview
Adapter Modules for Foundation Models
Low-Rank Adaptation (LoRA)
Prompt Tuning and Prefix Tuning
Comparing PEFT and Meta-Learning Approaches
Hybrid Adaptation Strategies
Hands-on Practical: Adapting a Foundation Model using LoRA
Chapter 6: Scaling Meta-Learning Implementations
Computational Challenges of Meta-Gradients
Memory Optimization Techniques
Distributed Meta-Learning Strategies
Efficient Task Sampling and Batching
Approximation Methods for Scalability
Benchmarking Scalable Implementations
Chapter 7: Advanced Topics and Theoretical Considerations
Bayesian Meta-Learning Approaches
Continual Meta-Learning
Meta-Learning for Reinforcement Learning
Generalization Bounds in Meta-Learning
Information Theoretic Perspectives
Open Problems and Research Directions
Advanced Topics and Theoretical Considerations
Having covered the primary categories of meta-learning algorithms, specific adaptation techniques for foundation models, and the practicalities of scaling these methods, this chapter examines several specialized areas and the theoretical foundations supporting meta-learning.
You will learn about:
- Probabilistic approaches to meta-learning using Bayesian methods, enabling uncertainty quantification in adaptation.
- Strategies for continual meta-learning, where models adapt to sequences of tasks without forgetting previous ones.
- The application of meta-learning principles within reinforcement learning (Meta-RL) to accelerate policy acquisition.
- Theoretical analyses concerning generalization bounds in meta-learning, aiming to understand how performance on meta-training tasks translates to new, unseen tasks.
- Connections between meta-learning and information theory, providing different perspectives on the learning process.
- Current open problems and research directions, highlighting areas needing further investigation, particularly concerning large-scale models.
This chapter provides a broader perspective on the meta-learning field, connecting practical algorithms to their theoretical underpinnings and exploring active research frontiers.
Sections
7.1 Bayesian Meta-Learning Approaches
7.2 Continual Meta-Learning
7.3 Meta-Learning for Reinforcement Learning
7.4 Generalization Bounds in Meta-Learning
7.5 Information Theoretic Perspectives
7.6 Open Problems and Research Directions
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